“Entrepreneurs just guess – and if they succeed, they say that they had data”

I was talking with one of the FirstOfficer.io testers, a smart and successful entrepreneur, when he said, “People are just guessing, aren’t they?”

“They want to look good, so they say that they had data, but I think they just guessed. And just look at what happened to Ron Johnson when he went from Apple to J.C. Penney. Even the big names fail.”

That point of view was totally new to me.

Finance and metrics are as much art as they are science. And when you understand data-based decision-making, you’ll know it always requires also vision and guts. But if you aren’t using data to back up your decisions now, you can have hard time imagining what actually can be done with it.

Not knowing that you don’t know is a risk

When I was a teen, my dad had me to do the company books. But a couple of years later when I went to study accounting I totally failed my first exam. Why? Because I thought I already knew all about accounting. My practical knowledge and confidence blinded me from seeing that the topic had much more depth than I could have imagined.

The biggest risks in your business comes from the things you don’t know that you don’t know.

Trying to use data to run your business if you don’t know the basics is risky. You’ll be confidently making bad decisions.

So let’s go through a couple of basic concepts. This is not specific to SaaS metrics, but general finances/metrics stuff.

It’s not a choice between guessing and knowing

When you use data to help you decide things, it does not mean that you would gain 100% knowledge. That’s just impossible. You’ll increase the probability that your guesses will be right. And you’ll manage the risks better. But there’s always some guesswork included.

You’ll go from a total guess to knowing that if things X,Y and Z will stay the same, you’ll reach your target. But you may also know that if thing X changes, it will be critical and you will fail. And there’s no way to ensure that things wouldn’t change.

As you can see, even when you have the data you cannot know the final outcome of your decision. But at that point, it’s hardly a guess either. It’s something in between. An educated guess, maybe?

You can’t separate your guts and the data

How you interpret the data depends on your skills, past experience and even your personality. You can give the same metrics dashboard to two different people and they see different things. It’s like everyone would look at the data through tinted lenses.

And you tend to ignore what you can’t understand.

For example, when I didn’t know how to read balance sheets, I thought it was just a trivial report that the law required us to print out every month. I thought that only the P&L sheet mattered. But it was just me who couldn’t even imagine all its purposes.

Numbers are not facts

You may think that numbers are facts. But when we are talking about finances and metrics, they aren’t. They are just a rough way to try to model the reality.

You’d think that profit from your P&L sheet was a hard number. Surely what you earn is a fact. But it isn’t. It’s your accountant’s best effort to match what really happens.

And the same goes for the SaaS metrics, but with somewhat smaller impact. Your last month MRR may contain people that end up asking full refund in the future. And how did you categorize that person who bought 3 subscriptions and then cancelled 2? Were those downgrades? Lost subscriptions? Or a clear mistake that should not be visible in the figures at all?

The only number-related thing that’s really a fact is the number of dollars in your bank account. Cash is the king.

If this sounds too strange, there’s an excellent book called Financial Intelligence that teaches basic finances to entrepreneurs. It’s targeted to larger non-SaaS companies with accrual accounting, but it’s still a great read, especially if you are also interested in investing.

Past performance does not guarantee future results

Some of the SaaS metrics are in fact future-looking projections, like customer lifetime value (CLTV). You are taking figures from your historical data and trying to estimate what will happen in the future.

It’s impossible to produce fully reliable CLTV figures.

For starters, there is a handful of different formulas to use, each one optimized for certain conditions. Churn rate, a major component in calculating the CLTV has lots of room for interpretation and inaccuracy too.

Secondly, even if the figure would be somewhat right, the behavior of your past customers can never predict how your existing or future customers will behave.

But having figures that are not perfect is OK – you just need to know that.

When you know which numbers are trustworthy and which are not, you can team up reliable figures together with the weak ones. That way you can come up with a prediction that you can actually work with.

Following up the numbers vs. proactively using them

I hope this post has given you a glimpse to the concepts behind data-based decision-making.

It has been educating for me too. I can now see what a huge jump it can be to go from following up your metrics to actually effectively using them in decision-making. That information will definitely help me in FirstOfficer.io’s future development – I don’t want it to be just another metrics dashboard. I want it to produce real results.

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[…] I think the number 3 is nicely covered with available tools, but numbers 1-2 are where people struggle. You’ll know you have this problem, when you feel that you need to guess things to improve your business. […]